Technology

ADC Background

Work on EViP technology was initiated by ADC’s founder, Dr. Tuan A Duong, while he was working at NASA’s Jet Propulsion Laboratory, in one of three frontier neural network teams in the United States (including AT&T Bell Labs and Bellcore) in 1984.  

Some of the relevant projects Dr. Duong researched and developed during his time at JPL include:

* Real-Time Mars Landing Site Identification  based on real-time color segmentation and adaptation, supported by Self-Evolving Neural Network Architecture namely Cascade Error Projection, to survey and identify a safe and productive landing site in real-time;

* Self-Evolving Neural Network Architecture Supervised Learning algorithm to identify Amino Acid building blocks for Life Detection Mission.

* Space Invariant Independent Component Analysis (SPICA) for recovering the original odorant sources from unknown mixtures for ENose (a multi-element chemical sensor) in an open unknown environment (Caltech patent).

* Introductory Extended Visual Pathway Data Flow, a technology which has now been fully developed at ADC (Caltech patent).

* Cognitive Computing Architecture that enables a general-purpose neural processor chip to be equipped with a compiler, making low power, compactness, and real-time adaptive operation available in a single package.  This set a cornerstone for intelligent perception and recognition in hardware implementation (Caltech patent).

* Others.

With his 12 patents with NASA or Caltech Assignee, 3 patents with ADC Assignee, and nine of them are neural networks related technology.

These technologies have provided the foundation for the technologies ADC has now brought to fruition.  NASA/JPL-Caltech provided generous support and an excellent environment for doing this preliminary research.  Involvement through licensing and other arrangements continues to be a key to ADC’s success.

Our Technology

At Adaptive Computation LLC, Dr. Tuan A Duong invented the Extended Visual Pathway (EViP) approach as an unsupervised learning approach to integrate a saccadic eye movement emulator with a bio-inspired visual processing pathway to enable the detection and recognition of generic full/partial/low resolution/ sketched/degraded objects in open and ambiguous environments.  This basic technology is protected by US and international patents.

He also invented a new learning architecture to enable the machine to self-learn new objects autonomously and additively in a sequential manner when the objects arrive and appear at different times. Hence the cognitive and perceptive capability can be equipped for machine intelligence. 

Extended Visual Pathway (EViP)

EViP consists of a saccadic eye movement emulator and visual pathway filters and visual cortex.

Unsupervised learning (EViP.1)

Bio-inspired Extended Visual Pathway (EViP) software, which integrates a saccadic eye movement emulator with an advanced model of the human visual pathway, to enable real-time processing for the detection and recognition of single or multiple objects in real-time and on-line, given inputs that are:

  • partial-view or full-view
  • low resolution or “noisy”
  • incomplete or “collage” style
  • sketches of actual objects.

to search for similar objects in an uncontrolled environments. This enables real-time adaptive capability, to serve as a short-term memory operations.

Self and Dynamic Supervised Learning (EViP.2)

Problems with the current supervised learnings: 1) required all training data ready; 2) During the training phase, it is manned in the loop; 3) it cannot update the new objects, new updated data, based on the previous knowledge unless to restart all over again; 4) it is only ML; hence, it is memorized with some interpolation capability, but no intelligence.

ADC approach enables:

  • Dynamic architecture based on the task, hence it is optimal
  • Self-learning and sequential learning as data arriving
  • New objects and/or new updated data can be accommodated from the previous learning knowledge
  • It is facilitated for machine intelligence

These features facilitate the capability of on-line learning to capturing and growing knowledge of previous knowledges or initiating and constructing knowledge of cognitive capabilities; hence, auto intelligence can be extracted.  This can be viewed as long-term memory operations.

Dynamic Supervised Learning Algorithm and Architecture (DSLAA)

Complete Autonomous and Adaptive Learning System (CAALS)

The closed loop between short-term and long-term memory operation set off the auto intelligence systems.

http://www.youtube.com/watch?v=fQO5xm9Drsk

Bio-Inspired Sparse Imager (BISI)

Benefits: reducing color image to sparse gray scale with 61.5x pixel intensity reduction while maintaining the performance in YOLO-x and ResNet-x using BDD100K data set.

Massive Parallel In-Memory Learning and Processing Architecture (MPIMLPA)

Benefits:

  • Learning fast, at least 5 orders of magnitude (O (5)) for DNNs as compared with software 
  • Processing improvement at least O (5)
  • Power consumption can be reduced under manageable budgets (e.g. less than a watt, depending on submicron feature size)

Reconfigurable Intelligent Search Engine (RISE)

Reconfigurable Intelligent Search Engine (RISE) is an implementation of EViP via Real-Time Extraction Engine (ReTEE) and architecture.

Benefits: can process 1000 frames/sec (each frame is 1Kx1K) with ROI included.

This is an illustration only.